Correlated information recommendation

ABSTRACT

Method and apparatus for information recommendation are provided. In one aspect, a method for information recommendation uses correlated information combinations to improve recommendation accuracy. Upon receiving data from a client indicating a visited information type, the method obtains correlated information types related to the visited information type from stored records. The correlated information types providing one or more correlated information combinations each including at least two correlated information types. For each of the one or more correlated information combinations, the method computes a degree of correlation between the correlated information combination and the visited information type. The method selects a target correlated information combination with a satisfying degree of correlation, and recommends the target correlated information combination to the client.

RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 12/663,243, filed Dec. 4, 2009, which claimspriority to the national stage application of international patentapplication PCT/US09/51152 filed Jul. 20, 2009, entitled “CORRELATEDINFORMATION RECOMMENDATION”, which claims priority from Chinese patentapplication, Application No. 200810129954.7, filed Jul. 24, 2008,entitled “METHOD AND APPARATUS FOR INFORMATION RECOMMENDATION”, whichapplications are hereby incorporated in their entirety by reference.

TECHNICAL FIELD

The present disclosure relates to fields of networking technologies, andparticularly to methods and apparatuses for information recommendation.

BACKGROUND

Along with the widespread use of the Internet, information resources onthe Internet have been expanding exponentially, causing problems of“information overload” and “information disorientation”. A user mayoften be lost in a space of tremendous information, and cannot smoothlyfind required information. Therefore, Internet-oriented technologiessuch as information search, information filtering and collaborativefiltering have emerged. One example is e-commerce recommendationsystems. These e-commerce recommendation systems directly interact witha user, simulate a salesperson of a shop to provide merchandiserecommendation to the user, and help the user to find needed merchandiseand complete the purchase process. The existing recommendation systemsare developed using real-life examples, e.g., product recommendationthrough another product, information recommendation through otherinformation, and group recommendation through another group. Theserecommendation systems, however, do not have wide enough coverage, orhigh enough accuracy. Under an ever-intensifying competitionenvironment, existing recommendation systems may incur a loss ofcustomers because of these problems, negatively impacting sales volumeand browsing volume of a website.

SUMMARY

The present disclosure provides a method that uses correlatedinformation combinations to improve recommendation accuracy. Uponreceiving data from a client indicating a visited information type, themethod obtains correlated information types related to the visitedinformation type from stored records. The correlated information typesproviding one or more correlated information combinations each includingat least two correlated information types. For each of the one or morecorrelated information combinations, the method computes a degree ofcorrelation between the correlated information combination and thevisited information type. The method then selects a target correlatedinformation combination which has a satisfactory degree of correlation,and recommends the target correlated information combination to theclient. The degree of correlation between the correlated informationcombination and the visited information type may be computed based on anoccurrence property of the correlated information combination.

The visited information type may include one or more of merchandiseinformation, blog information, group information, post information,product information, news information, message information, keywordinformation, and advertisement information.

In one embodiment, to obtain the correlated information types related tothe visited information type, the method obtains from the stored recordsone or more other clients which have used the visited information type,and obtains from the stored records one or more other information typeswhich have been used by the other clients.

In one embodiment, the method obtains user information of the currentclient and user information of one or more other clients which have usedthe visited information type, and obtains from the stored records one ormore other information types which have been used by the other clients.To compute the degree of correlation between a correlated informationcombination and the visited information type, the method furtherdetermines a weight coefficient for the correlated informationcombination according to the user information of the current client andthe user information of the other clients which have used the visitedinformation type, and computes the degree of correlation between thecorrelated information combination and the visited information typebased on an occurrence property of the correlated informationcombination and the weight coefficient.

Another aspect of the present disclosure is an apparatus implementingthe disclosed method. The apparatus may have a server computer or bepart of a server computer. The disclosed method and apparatus facilitateinteractions among various information flows and customizedrecommendations, and may improve the accuracy of a recommendationsystem.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DESCRIPTION OF DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items.

FIG. 1 shows a flow chart of an exemplary method for informationrecommendation in accordance with the present disclosure.

FIG. 2 shows a flow chart of the method for information recommendationin an exemplary application environment.

FIG. 3 shows a structural diagram of an apparatus for informationrecommendation in accordance with the present disclosure.

DETAILED DESCRIPTION

According to one embodiment of the present disclosure, the method forinformation recommendation obtains visited information type of a client,obtains a plurality of correlated information types related to thevisited information type from stored records, and obtains an occurrenceproperty of one or more correlated information combinations of at leasttwo correlated information types. The method then computes, for each ofat least some of the correlated information combinations, a degree ofcorrelation between the correlated information combination and thevisited information type, selects from the one or more of the correlatedinformation combinations a target correlated information combinationwhose degree of correlation satisfies a requirement, and recommends thetarget correlated information combinations to the client. Multipletarget correlated information combinations may be selected andrecommended.

FIG. 1 shows a flow chart of an exemplary method 100 for informationrecommendation in accordance with the present disclosure. In thisdescription, the order in which a process is described is not intendedto be construed as a limitation, and any number of the described processblocks may be combined in any order to implement the method 100, or analternate method. The exemplary method 100 illustrated in FIG. 1 isdescribed as follows.

At Block 101, a client sends data indicating a visited information typeto a server. For example, the client sends merchandise information orblog information to the server to indicate which merchandise or blog hasbeen visited or used by a current user. In the exemplary embodiments ofthe present disclosure, visited information type may include, but arenot limited to, one or more of Internet information entity such asmerchandise information, blog information, group information, postinformation, product information, news information, message information,keyword information, and advertisement information.

At this block, the client may further send user information of thecurrent user at the client to the server. For instance, a registrationname of the client's user may be sent to the server. The server mayobtain such user information as education level, career information, andage information of the user based on the registration name of theclient's user, and prepares a second mining according to the userinformation of the client.

At Block 102, the server obtains a plurality of correlated informationtypes related to the visited information type from stored records. Forexample, the server first obtains records of one or more other clientswhich have used or visited the current visited information type.Examples of using or visiting a certain information type include, butare not limited to, such acts of as browsing, saving, buying,commenting, processing, joining, and recommending the information type.The server then obtains from the stored records the other informationtypes that have also been used or visited by these clients. Examples ofthese various information types may include any one or more ofmerchandise information, blog information, group information, postinformation, product information, news information, message information,keyword information, and advertisement information. The server has thuslearned about various other information types that have been used by theone or more other clients who have also used the same current visitedinformation type. These various other information types are consideredto be correlated or related to the current visited information type. Theserver therefore obtains a variety of correlated information typesrelated to the current visited information type this way. Thesecorrelated information types provide one or more correlated informationcombinations each including at least two correlated information types.The correlated information combinations are then used in a manner asdescribed below.

At Block 103, the server obtains an occurrence property of eachcorrelated information combination of at least two correlatedinformation types, and computes for the correlated informationcombination a degree of correlation between the correlated informationcombination and the visited information type. This may be done eitherfor every correlated information combination provided by the correlatedinformation types, or for just a selected number of correlatedinformation combinations. The occurrence property of a correlatedinformation combination may include one or more of a frequency ofoccurrence, a time of occurrence, and a place (e.g., a platform) ofoccurrence.

Here, the server may first select correlated information combinationsaccording to an application scenario of the client at Block 102. Forexample, according to the application scenario of the client, the servermay select correlated information combinations which have two or morecorrelated information types such as merchandise information, bloginformation, group information, post information, product information,news information, message information, keyword information, andadvertisement information. The application scenario may indicate thatcertain information types are undesired or unnecessary, while certainother information types are desired or necessary. The correlatedinformation combinations of the desired or necessary information typesare then selected. For example, either a correlated informationcombination of the merchandise information and news information, or acorrelated information combination of the blog information, groupinformation and post information may be selected, depending on theapplication scenario. The occurrence properties of each correlatedinformation combination are then surveyed (e.g., counted using anysuitable statistical method), and are used for computing the degree ofcorrelation between a respective correlated information combination andthe visited information type.

A single occurrence property such as the frequency of occurrence foreach correlated information types may first be obtained to obtain afrequency of occurrence for each correlated information combination. Thedegree of correlation between each correlated information combinationand the visited information type may then be computed based on therespective frequency of occurrence for the correlated informationcombination. Alternatively, multiple currency properties such as thefrequency and the time of occurrence for each correlated informationtype may first be obtained to obtain the frequency of occurrence and thetime of occurrence of each correlated information combination. Thedegree of correlation between each correlated information combinationand the visited information type may then be computed based on therespective frequency and time of occurrence of the correlatedinformation combination.

Here, a second mining may be performed using the user information of theclient obtained at Block 101. From the correlated information typesobtained at Block 102, the server may obtain occurrence properties ofcorrelated information combinations of at least two correlatedinformation types that satisfy the requirements of the applicationscenario of the client. For example, the server may obtain an occurrenceproperty of each correlated information combination that has bloginformation, group information and post information according to certainapplication scenario.

Upon obtaining the user information of the current client and the userinformation of other clients which have used the same visitedinformation type, the server may determine weight coefficients for theoccurrence property of each correlated information combination based onthe information of the client and the information of the client(s)having used the visited information type. Finally, the server computes adegree of correlation between each correlated information combinationand the visited information type based on the occurrence property of therespective correlated information combination and the correspondingweight coefficient.

At Block 104, the server selects a target correlated informationcombination which has a satisfactory degree of correlation from thecorrelated information combinations and sends the target associatedinformation combination to the client. One or more correlatedinformation combinations may be selected and sent in this manner.

Here, the degrees of correlation which have been computed at Block 103can be ranked for the purpose of selection. Based on a ranking result,one or more correlated information combinations having a degree ofcorrelation with a higher rank are selected and recommended to theclient. Alternatively, the degrees of correlation which are computed atBlock 103 may be compared with a set value. If a degree of correlationis greater than the set value, the respective correlated informationcombination is selected and recommended to the client.

At this block, the selected target correlated information combinationmay be sent to the client after having been converted to a correspondinghypertext format needed according to the application scenario of theclient. For example, the server may send to the client one or more ofthe correlated information combinations and their correspondinghypertext format.

At Block 105, the client displays the received correlated informationcombination. For example, one or more of the correlated informationcombinations recommended at Block 104 and their corresponding hypertextformats are displayed to the user.

FIG. 2 shows a flow chart of the method 200 for informationrecommendation in an exemplary application environment. In FIG. 2,merchandise information is used as an example of the visited informationtype. However, it should not be construed that the method 200 of thepresent disclosure can only be used for recommending merchandiseinformation. Recommendation for any Internet information entity such asblog information, group information, post information, productinformation, news information, message information, keyword information,and advertisement information can also be implemented in similar mannersto the exemplary embodiments of the present disclosure. The exemplarymethod 200 of FIG. 2 is described as follows.

At Block 201, a user accesses a web page having merchandise informationthrough a client. For example, the user may desire to buy merchandise Aby accessing the webpage.

At Block 202, the client used by the user sends information of themerchandise A and user information to a server. For the purpose of usingthe disclosed method, the information of the merchandise A may only needto indicate that the user is interested in buying the merchandise A orhas visited merchandise A, and no full description of the merchandise Amay be necessary.

At Block 203, upon receiving the information of the merchandise A andthe user information, the server obtains from stored records informationof other users who are related to the information of the merchandise Awithin a certain period of time. For example, the server may obtainother users who have bought, browsed, or collected the merchandise Awithin the last month. For the purpose of illustration, assume that suchuses include user M1, user M2, user M3, user M4, and user M5. The serverfurther obtains various other information types that have been used byM1, M2, M3, M4, and M5. For example, in addition to buying merchandiseA, user M1 may have also bought merchandise B, merchandise C, andmerchandise D, browsed blog1, blog2, blog3, advertisement 1,advertisement2, advertisement3, and advertisement4, and joined group1and group2. User M2 may have also bought merchandise B, merchandise C,and merchandise E, browsed blog1, blog2, blog4, advertisement1,advertisement2, and advertisement3, and posted post1 and post2. User M3may have also browsed merchandise B, merchandise D, merchandise F,blog1, blog4, and blog5, joined group1, and posted post1 and post3. UserM4 may have also collected merchandise B, merchandise F, and merchandiseG, browsed blog2, news1, news2, and news3, joined group2, and postedpost3 and post4. User M5 may have also bought merchandise B andmerchandise F, browsed blog2, blog6, and advertisement4, joined group1and group3, and browsed news1 and news4. These various additionalinformation types are considered correlated information types of theinformation of merchandise A, that is, information types that arecorrelated to the information of merchandise A. At this block, theserver may further obtain one or multiple kinds of user information suchas age information, education level, career information, and familyinformation of the users M1, M2, M3, M4, and M5.

At Block 204, according to an application platform of the web page thatthe user visits, correlated information combinations satisfying theapplication platform are selected from the combinations of at least twoinformation types obtained. If the accessed web page contains only acombination of merchandise information and news information, forexample, the combination of the merchandise information and the newsinformation is selected to be correlated information combination. If theaccessed web page contains a combination of merchandise information,blog information, advertisement information, and news information, thecombination of the merchandise information, the blog information, theadvertisement information, and the news information is then selected tobe correlated information combination. That is, based on an applicationenvironment of the web page that the user visits, the method selectscorrelated information combinations which satisfy the applicationenvironment. The selected correlated information combination each has atleast two information types which are compatible to the applicationenvironment. The current example uses an accessed web page havingmerchandise information and news information for illustration.Therefore, the combination of merchandise information and newsinformation is selected to be a correlated information combination.Because there are multiple kinds of merchandises and news, the selectedcorrelated information combination may represent multiple combinationsof specific merchandises and news.

At Block 205, occurrence properties of the various combinations of themerchandise information and the news information are surveyed. Thecurrent example uses the frequency of occurrence as an example ofoccurrence property for illustration.

In an illustrative example, among the users who have bought, browsed, orcollected the merchandise A, two users have bought, browsed or collectedthe merchandise B, and browsed news1. Accordingly, the frequency ofoccurrence of the combination of the merchandise B and news1 is two. Twousers have bought, browsed or collected the merchandise F and browsednews1. Accordingly, the frequency of occurrence of the combination ofmerchandise F and news1 is two. One user has bought, browsed orcollected the merchandise B, and browsed news2. Accordingly, thefrequency of occurrence of the combination of the merchandise B andnews2 is one. One user has bought, browsed or collected the merchandiseB, and browsed news3. Accordingly, the frequency of occurrence for themerchandise B and news3 is one. One user has bought, browsed orcollected the merchandise B, and browsed news4. Accordingly, thefrequency of occurrence of the combination of the merchandise B andnews4 is one. One user has bought, browsed or collected the merchandiseF, and browsed news2. Accordingly, the frequency of occurrence of thecombination of the merchandise F and news2 is one. One user has bought,browsed or collected the merchandise E, and browsed news3. Accordingly,the frequency of occurrence of the combination of the merchandise F andnews3 is one. One user has bought, browsed or collected the merchandiseF, and browsed news4. Accordingly, the frequency of occurrence of thecombination of the merchandise F and news4 is one. One user has bought,browsed or collected the merchandise G, and browsed news1. Accordingly,the frequency of occurrence for the merchandise G and news1 is one. Oneuser has bought, browsed or collected the merchandise G, and browsednews2. Accordingly, the frequency of occurrence of the combination ofthe merchandise G and news2 is one. One user has bought, browsed orcollected the merchandise G, and browsed news3. Accordingly, thefrequency of occurrence of the combination of the merchandise G andnews3 is one.

At Block 206, based on the statistics results obtained at Block 205, theserver computes a degree of correlation between each combination ofmerchandise/news information and the visited information type (which inthe present example is buying the merchandise A) using a suitablecorrelation algorithm. The computed degrees of correlation indicate thedegree of correlation between each combination merchandise/newsinformation and buying of the merchandise A. For example, arecommendation algorithm using collaborative filtering may be used tocompute the degrees of correlation. Alternatively, a recommendationalgorithm having preconfigured business rule(s) may be used.

From the statistical results, it can be seen that the combination of themerchandise B and news1, and the combination of the merchandise F andnews1 co-occur with the merchandise A most frequently, each havingco-occurrence frequency of two. The degrees of correlation between theseaccommodations and the merchandise A may be determined to be two basedon the respective frequency of occurrence. Similarly, the degree ofcorrelation between the combination of the merchandise B and news2 andthe merchandise A is one. The degree of correlation between thecombination of the merchandise B and news3 and the merchandise A is one.The degree of correlation between the combination of the merchandise Band news4 and the merchandise A is one. The degree of correlationbetween the combination of the merchandise F and news2 and themerchandise A is one. The degree of correlation between the combinationof the merchandise F and news3 and the merchandise A is one. The degreeof correlation between the combination of the merchandise F and news4and the merchandise A is one.

In some embodiments, the method may perform a second mining based on theuser information which has been sent from the client. For example,further mining may be performed on the above computed degrees ofcorrelation using the education levels of the users. For example, uponsurveying education levels of the users who have browsed or collectedthe merchandise A, it may be observed that highly educated users have ahigher interest in the merchandise B and news1. If the user informationreturned from the current client shows that the associated user is alsohighly educated, a higher weight coefficient may be signed to thefrequency of occurrence of the combination of the merchandise B andnews1. For instance, the weight coefficient for the frequency ofoccurrence of the combination of the merchandise B and news1 may be setto be one, while the weight coefficient for the frequency of occurrenceof the merchandise F and news1 may be set to a relatively lower 0.6. Assuch, a result obtained after further mining the user information mayshow that the degree of correlation between the combination ofmerchandise B and news1 and the present visited merchandise A is thehighest at a value of 2, while the degree of correlation between thecombination of merchandise F and news1 and the merchandise A is thesecond highest at a value of 1.2 (2×0.6=1.2). The weight coefficientsfor the other combinations of the merchandise information and the newsinformation may be assigned in a similar manner. The degree ofcorrelation between various combinations of the merchandise informationand the news information and the merchandise A are obtained based on therespective frequencies of occurrence and weight coefficients.

At Block 207, the degrees of correlation which have been computed atBlock 206 are compared with a set value. If a degree of correlation isgreater than the set value, the combination corresponding to this degreeof correlation is selected. In the illustrated example, the set valuefor the degree of correlation is one. Accordingly, the combinationshaving degree of correlation greater than the set value include thecombination of the merchandise B and news1 and the combination of themerchandise F and news1.

In one embodiment, the degrees of correlation between the correlatedinformation combinations and the visited information type may be rankedaccording to their values in order to select correlated informationcombinations corresponding to a certain number of degrees of correlationaccording to the ranking. For example, the correlated informationcombinations with the two highest degrees of correlation in the rankingmay be selected.

At Block 208, the server sends one or more of the correlated informationcombinations selected at Block 207 to the client. The server convertsthe combination of the merchandise B and news1 and the combination ofthe merchandise F and news1 into their respective hypertext format. Theserver then sends the combination of the merchandise B and news1, thecombination of the merchandise F and news1, and the respective hypertextformats to the client.

Alternatively, the server may send the correlated informationcombinations corresponding to a certain number (N) of degrees ofcorrelation, and the respective hypertext formats to the client.

At Block 209, the client displays the combination of the merchandise Band news1 and the combination of the merchandise F and news1 with theirrespective requested hypertext formats to the user. Alternatively, theclient may display the correlated information combinations correspondingto a certain number of degrees of correlation with the respectiverequested hypertext formats to the user.

An apparatus for information recommendation is further provided toimplement the disclosed method. The above-described techniques may beimplemented with the help of one or more computer-readable mediacontaining computer-executable instructions. The computercomputer-executable instructions enable a computer processor to performa competitive resource allocation in accordance with the techniquesdescribed herein. It is appreciated that the computer readable media maybe any of the suitable memory devices for storing computer data. Suchmemory devices include, but not limited to, hard disks, flash memorydevices, optical data storages, and floppy disks. Furthermore, thecomputer readable media containing the computer-executable instructionsmay consist of component(s) in a local system or components distributedover a network of multiple remote systems. The data of thecomputer-executable instructions may either be delivered in a tangiblephysical memory device or transmitted electronically.

FIG. 3 shows a structural diagram of an apparatus 300 for informationrecommendation in accordance with the present disclosure. Asillustrated, the apparatus 300 includes a receiving unit 310, acomputation unit 320, and a recommendation unit 330.

The receiving unit 310 is used for obtaining from stored recordscorrelated information types related to a visited information typeindicated by a client. The correlated information types provide one ormore correlated information combinations each including at least twocorrelated information types.

The computation unit 320 is used for obtaining an occurrence property ofeach of the one or more correlated information combinations, andcomputing a degree of correlation between the correlated informationcombination and the visited information type.

The recommendation unit 330 is used for selecting from the one or morecorrelated information combinations a target correlated informationcombination whose degree of correlation satisfies a requirement, andrecommending the target correlated information combination to theclient.

The apparatus 300 may further include a storage unit 340 used forstoring the stored records containing information that have been used bythe current client and one or more other clients.

In one embodiment, the computation unit 320 is further used forselecting the one or more correlated information combinations eachhaving at least two kinds of correlated information types from the aplurality of correlated information types based on an applicationscenario of the client. The computation unit 320 may also be adapted fordetermining the occurrence properties of each correlated informationcombination.

The receiving unit 310 may be further adapted for obtaining userinformation of the current client and user information of one or moreother clients which have used the visited information type, and forobtaining one or more other information types which have been used bythe other clients from the stored records stored in the data storage340. In this embodiment, the computation unit 320 may be further usedfor determining a weight coefficient for each of the one or morecorrelated information combinations according to the user information ofthe current client and the user information of the one or more otherclients which have used the visited information type. The computationunit 320 may further compute the degree of correlation between eachcorrelated information combination and the visited information typebased on an occurrence property of the respective correlated informationcombination and the weight coefficient.

In one embodiment, the recommendation unit 330 is further used forranking the correlated information combinations according to theirdegrees of correlation with the visited information type, and furtherselecting a target correlated information combinations according to theranking of the degrees of correlation. Alternatively or additionally,the recommendation unit 330 may be used for comparing the degrees ofcorrelation with a set value, and selecting from the correlatedinformation combinations a target correlated information combinationwhose degree of correlation is greater than the set value.

The apparatus 300 may be further adapted for converting a correlatedinformation combination into a hypertext format, and sending thehypertext format to the client.

Herein, a “unit” is a device which is a tool or machine designed toperform a particular task or function. A unit or device can be a pieceof hardware, software, a plan or scheme, or a combination thereof, foreffectuating a purpose associated with the particular task or function.In addition, delineation of separate units does not necessarily suggestthat physically separate devices are used. Instead, the delineation maybe only functional, and the functions of several units may be performedby a single combined device or component. When used in a computer-basedsystem, regular computer components such as a processor, a storage andmemory may be programmed to function as one or more units or devices toperform the various respective functions.

The apparatus 300 may be a backend server or implemented in such aserver. Upon obtaining visited information type of a client, the serverobtains a plurality of correlated information types related to thevisited information type from stored records, and an occurrence propertyof each correlated information combination of least two correlatedinformation types. The server then computes for each correlatedinformation combination a degree of correlation between the correlatedinformation combination and the visited information type, selects one ormore of the correlated information combinations whose degree ofcorrelation satisfies a requirement, and recommends the selectedcorrelated information combinations to the client. The server mayfurther perform a second mining for user information of the client, andhence can recommend the most suitable information to the user. Based onany input of the user, the server outputs different recommendationresults correlated to the input. The input may be information of a groupof people, a discussion or chat group, merchandise, a blog, a post, adescription of a product, a message or news. The output may be anycombination of information of a group of people, a discussion or chatgroup, merchandise, a blog, a post, a description of a product, amessage or news. The method is applicable to a variety of Internetinformation entities to accomplish interactions among variousinformation flows, customize recommendations, and improve therecommendation accuracy and coverage of recommended information. Properuse of the method may improve the sale volume and browsing volume of awebsite.

It is appreciated that the potential benefits and advantages discussedherein are not to be construed as a limitation or restriction to thescope of the appended claims.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A method for information recommendation, themethod comprising: receiving data from a client indicating a visitedinformation type; obtaining a plurality of correlated information typesrelated to the visited information type from stored records, theplurality of correlated information types providing one or morecorrelated information combinations each including at least twocorrelated information types; for each of the one or more correlatedinformation combinations, computing a degree of correlation between thecorrelated information combination and the visited information type; andselecting from the one or more correlated information combinations atarget correlated information combination whose degree of correlationsatisfies a requirement, and recommending the target correlatedinformation combination to the client.
 2. The method as recited in claim1, wherein the degree of correlation between the correlated informationcombination and the visited information type is computed based on anoccurrence property of the correlated information combination.
 3. Themethod as recited in any one of claim 2, wherein the occurrence propertycomprises one or more of a frequency of occurrence, a time ofoccurrence, or a platform of occurrence.
 4. The method as recited inclaim 1, wherein the visited information type includes one or more ofmerchandise information, blog information, group information, postinformation, product information, news information, message information,keyword information, or advertisement information.
 5. The method asrecited in claim 1, wherein computing the degree of correlation betweeneach of the one or more correlated information combinations and thevisited information type comprises: selecting the one or more correlatedinformation combinations based on an application scenario of the client;determining an occurrence property of each of the one or more correlatedinformation combinations; and computing the degree of correlationbetween each of the one or more correlated information combinations andthe visited information type using the occurrence property.
 6. Themethod as recited in claim 1, wherein obtaining the plurality ofcorrelated information types related to the visited information typecomprises: obtaining from the stored records one or more other clientswhich have used the visited information type; and obtaining one or moreother information types which have been used by the one or more otherclients from the stored records.
 7. The method as recited in claim 1,further comprising: obtaining user information of the current client anduser information of one or more other clients which have used thevisited information type; and obtaining one or more other informationtypes which have been used by the one or more other clients from thestored records; wherein computing the degree of correlation between eachof the one or more correlated information combinations and the visitedinformation type further comprises: determining a weight coefficient forthe correlated information combination according to the user informationof the current client and the user information of the one or more otherclients which have used the visited information type; and computing thedegree of correlation between the correlated information combination andthe visited information type based on an occurrence property of thecorrelated information combination and the weight coefficient.
 8. Themethod as recited in claim 7, wherein the user information of thecurrent client and the user information of the one or more currentclients each include one or more of user age information, user educationlevel, user career information, and user family information.
 9. Themethod as recited in claim 1, wherein selecting from the one or morecorrelated information combinations a target correlated informationcombination comprises: ranking the one or more correlated informationcombinations according to the respective degree of correlation, andselecting a correlated information combination whose degree ofcorrelation is ranked among the highest to be the target correlatedinformation combination.
 10. The method as recited in claim 1, whereinselecting from the one or more correlated information combinations atarget correlated information combination comprises: comparing thedegree of correlation of each of the one or more correlated informationcombinations with a set value, and selecting a correlated informationcombination whose degree of correlation is greater than the set value tobe the target correlated information combination.
 11. The method asrecited in claim 1, further comprising: converting the target correlatedinformation combination into a hypertext format; and sending thehypertext format to the client.
 12. An apparatus for informationrecommendation, the apparatus comprising: a receiving unit configured toobtain from stored records a plurality of correlated information typesrelated to a visited information type indicated by a client, theplurality of correlated information types providing one or morecorrelated information combinations each including at least twocorrelated information types; an computation unit configured to obtainan occurrence property of each of the one or more correlated informationcombinations, and compute a degree of correlation between the correlatedinformation combination and the visited information type; and arecommendation unit configured to select from the one or more correlatedinformation combinations a target correlated information combinationwhose degree of correlation satisfies a requirement, and recommend thetarget correlated information combination to the client.
 13. Theapparatus as recited in claim 12, wherein the apparatus furthercomprises: a storage unit configured to store the stored recordscontaining information that have been used by the current client and oneor more other clients.
 14. The apparatus as recited in claim 12, whereinthe computation unit is further configured to select the one or morecorrelated information combinations of at least two correlatedinformation types based on an application scenario of the client. 15.The apparatus as recited in claim 12, wherein the apparatus is furtheradapted to obtain user information of the current client and userinformation of one or more other clients which have used the visitedinformation type and obtaining one or more other information types whichhave been used by the other clients from the stored records, and whereinthe computation unit is further configured to: determine a weightcoefficient for each of the one or more correlated informationcombinations according to the user information of the current client andthe user information of the one or more other clients which have usedthe visited information type; and compute the degree of correlationbetween each of the one or more correlated information combinations andthe visited information type based on an occurrence property of therespective correlated information combination and the weightcoefficient.
 16. The apparatus as recited in claim 12, wherein theapparatus comprises a server computer.